What is the price of the Training MLflow - Master ML Tracking in 5 Days training?

The price of the Training MLflow - Master ML Tracking in 5 Days training is 5250 € HT. This includes access to all modules, training materials and personalized support. Funding solutions are available through OPCO.

Is the training Qualiopi certified?

Yes, Learni is a Qualiopi-certified training organization. This certification guarantees the quality of our courses and enables funding through OPCO and other mechanisms.

What is the duration of the Training MLflow - Master ML Tracking in 5 Days training?

The Training MLflow - Master ML Tracking in 5 Days training takes place over 35 hours. The program is structured in progressive modules for optimal skill development.

How can I fund my training?

Our courses are eligible for OPCO funding. Learni supports you through the funding process. A personalized quote is available upon request.

What are the prerequisites for this training?

The prerequisites for the Training MLflow - Master ML Tracking in 5 Days training are: Python basics, machine learning fundamentals. A preliminary interview validates your eligibility.

Is the training available in person?

Yes, the Training MLflow - Master ML Tracking in 5 Days training is available in remote. The format can be adapted to your needs (inter, intra, custom).

Training MLflow - Master ML Tracking in 5 Days | Learni

The story of Learni

Founded by passionate advocates of learning and innovation, Learni set out to make professional training accessible to everyone, everywhere in the world. Our team works in the largest cities such as Paris, Lyon, Marseille, and internationally, to support talents and organizations in their skills development.

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Methods, approaches, and teaching resources

The Training MLflow - Master ML Tracking in 5 Days training is delivered in-person or remotely (blended-learning, e-learning, virtual classroom, remote in-person). At Learni, a Qualiopi-certified training organization, each program is designed to maximize skills acquisition, regardless of the training mode chosen.

The trainer alternates between demonstrative, interrogative, and active methods (through practical exercises and/or real-world scenarios). This pedagogical approach ensures concrete and directly applicable learning in the workplace.

Teaching resources provided

To ensure the quality of the Training MLflow - Master ML Tracking in 5 Days training, Learni provides the following teaching resources:

Mac or PC computers, high-speed fiber internet, whiteboard or flipchart, video projector or interactive touchscreen (for remote sessions)
Training environments installed on workstations or accessible online
Course materials, practical exercises, and supplementary resources
Post-training access to materials and teaching resources

For in-house training at a location external to Learni, the client ensures and commits to having all necessary teaching materials (IT equipment, internet connection...) for the proper conduct of the training action in accordance with the prerequisites indicated in the communicated training program.

* contact us for remote feasibility** ratio varies depending on the training followed

Assessment methods

The assessment of skills acquired during the Training MLflow - Master ML Tracking in 5 Days training is carried out through:

During training: case studies, practical exercises, and professional scenarios
At the end of training: self-assessment questionnaire and skills evaluation by the trainer
After training: training completion certificate detailing acquired skills

Training accessibility

Learni is committed to the accessibility of its professional training programs. All our training programs are accessible to people with disabilities. Our teams are available to adapt teaching methods to your specific needs. Do not hesitate to contact us for any accommodation request.

Training access terms and deadlines

Training objectives

Install and configure MLflow in your environment

Create and manage Machine Learning experiments

Automatically log parameters, metrics, and artifacts

Visualize results via the MLflow UI interface

Integrate MLflow into a complete workflow

Deploy models with MLflow in production

Training program

Module 1Introduction to MLflow: installation and first runs (Python tools, UI)

Discover MLflow by installing the tool via pip, configure your tracking server, launch your first experiments with mlflow.start_run(), log simple parameters, explore the intuitive UI interface to visualize runs, complete practical exercises on basic datasets, produce your first reproducible deliverable that boosts your productivity from day 1.

Module 2Advanced Tracking: metrics and artifacts (logging, autologging)

Dive into logging metrics with mlflow.log_metric(), capture artifacts like graphs and files, enable autologging for scikit-learn, practice on concrete regression cases, compare runs in real-time via the UI, generate automated reports, end with a collaborative exercise that makes your experiments irrefutable and shareable.

Module 3Model Management: MLflow Model Registry (versioning, staging)

Master the Model Registry to register your models with mlflow.register_model(), manage versions and stages like Staging or Production, test smooth transitions, integrate with PyTorch or TensorFlow, apply to an image classification project, create validation pipelines, obtain a deployment-ready deliverable that secures your ML assets.

Module 4Practical Projects: end-to-end workflows (Git integration, CI/CD)

Build a complete workflow by integrating MLflow with Git and Jupyter, automate tracking in complex scripts, simulate real scenarios with hyperparameter tuning via MLflow, analyze performance comparisons, collaborate in a team on the shared UI, produce a portfolio of projects that impresses recruiters and colleagues.

Module 5Deployment and Best Practices: serving and monitoring (MLServer, production)

Deploy your models with mlflow models serve(), configure MLServer for scalable inference, implement post-deployment monitoring, review best practices for reproducibility, case studies on common failures avoided, finalize a personalized action plan, leave with skills ready to boost your corporate ML projects.